Agent-assisted software development – Building software with AI

From AI experiment to professional software development

AI is changing software development at speed. AI assistants and coding agents help developers build faster, search smarter, and automate repetitive work. But how do you make sure AI genuinely adds value without putting quality, security, and control under pressure?

AI tools are now part of daily practice for many developers. Even so, a good prompt on its own isn't enough. The real gains come when teams make AI part of their engineering approach with clear guidelines, solid context, feedback mechanisms, and appropriate quality controls. In this training, you'll discover how to organize exactly that, from prompt engineering to harness engineering and agent-ready architecture.

Do you work with GitHub Copilot, Codex, Claude Code, or a comparable tool? The training is tool-independent by design: you'll learn the principles that apply to every coding agent, supplemented with tips and best practices for your tool and its level of adoption within your organization. Theory and practice alternate throughout the day, with recognizable situations from everyday software development. Content and examples are tailored to your organization's tooling and context wherever possible.

The training is developed and delivered by Betabit specialists who use AI tooling daily to build business-critical software, in close collaboration with business and QA, and who guide organizations through the adoption of AI tooling.

What you’ll learn

During this training, you'll work with both the possibilities and the preconditions of agent-assisted software engineering:

• The fundamentals: LLMs and agents
  • How LLMs work and what sets agents apart
  • What agents can and can't do independently
  • The impact of AI on the role of the software developer
• Prompts and prompt engineering
  • Writing effective prompts
  • Providing context and steering results
  • Common pitfalls and patterns
• Development use cases
  • Applying AI to code generation, refactoring, and debugging
  • Working with existing codebases
  • Using AI for analysis, documentation, and knowledge discovery
  • The impact of AI on the development process
• Tooling in practice
  • Tips and best practices for coding agents
  • Tips and best practices for your tool (GitHub Copilot, Codex, Claude Code, or comparable)
  • Integrating AI into existing development workflows
• Harness engineering
  • Guides, sensors, and tools
  • Creating an environment in which agents work effectively and under control
  • Feedback loops and guardrails for AI-assisted development
• Risk and cost management
  • Managing the risks of AI-assisted development
  • Costs, token usage, and scalability
  • Context engineering: making the right information available at the right moment
• Agent-ready software architecture
  • Designing software suited to collaboration with agents
  • Architectural choices that support AI-assisted development
  • Preparing for a development process in which agents take on more and more tasks: how do you make the next step?

Software development with AI as an engineering discipline

After this training, you'll know what AI tools can do and, above all, how to apply them responsibly within your development process. You'll get straight to work with prompt engineering, context engineering, and harness engineering. And you'll know which steps are needed to prepare your team and your software for further agent-assisted development.